Suppose, I have a lot of nodes with small resources on memory and cpu maybe 5 or maybe 20.
These nodes are not really reliable, they may be switched of by the User.
They all use a database for readonly master data which will be delivered by a kafka topic connected to from each node.
What I want to achieve is to use infinispan as a distributed[replicated] cache above the database used by the nodes, so that at any node at any point on time has the same "view" on the readonly database.
Can I get this working, especially with low resources and if yes, is there any Link to an example for getting expirience?
Thanx
I don't think you can get a definite answer here, you need to try it out. I wouldn't call 5 - 20 CPUs small resources; there's not much going on in background when you're not actively reading/writing the cache so there shouldn't be any 'constant' overhead - just JGroups' heartbeat messages and such.
When using off-heap memory, Infinispan can be started with pretty small JVM heaps (24 MB IIRC, just for the POC), so you might be fine. However if you'll replicate the database on every node it's going to occupy some memory.
If the nodes often come and go, it could cause some churn on CPU. In replicated mode leaves won't matter too much, but when a node joins it will be getting all the data (from different nodes).
Related
Janusgraph does some internal activities due to which there are spikes in starting 1-2 runs.. (even at 10 QPS) and after that it gets stable. Once it's stable, no spikes are observed, is it expected behaviour, that needed some warm-up run to make it stable? Using Back-end Cassandra CQL.
Cassandra can exhibit behavior like this on startup. If there are files in the commitlog (directory), those will validate and write their data to disk. If there are a lot of them, there can be a bit of an increase in resource consumption.
While I am running nodetool decommission, I want to use 100% of my network. I set "nodetool setstreamthroughput 0". At the beginning, since the node on which decommission process started sends multiple nodes, The node can send data at speed 900Mbps. Later, since number of nodes that transferred is reducing, the node can send data like 300Mbps.
I see that the node sends one SSTable to one node. I want to increase the parallelism. nodetool says that one connection per hosts. How can I increase this setting. I mean "multiple connection per hosts" while I am streaming?
Most likely Cassandra 3.0 will not be able to utilize 100% of your network regardless of how you set it up. Even with multiple threads you push up against a point where the allocation rate of objects generated in the streaming will exceed what the jvm can clean up and then your GC pauses will only be able give you 100% for short periods. Kind of moot though as you cannot configure it to use more threads.
In cassandra 4.0 you will be able to achieve this: http://cassandra.apache.org/blog/2018/08/07/faster_streaming_in_cassandra.html
I am using Datastax Cassandra 4.8.16. With cluster of 8 DC and 5 nodes on each DC on VM's. For last couple of weeks we observed below performance issue
1) Increase drop count on VM's.
2) LOCAL_QUORUM for some write operation not achieved.
3) Frequent Compaction of OpsCenter.rollup_state and system.hints are visible in Opscenter.
Appreciate any help finding the root cause for this.
Presence of dropped mutations means that cluster is heavily overloaded. It could be increase of the main load, so it + load from OpsCenter, overloaded system - you need to look into statistics about number of requests, latencies, etc. per nodes and per tables, to see where increase happened. Please also check the I/O statistics on machines (for example, with iostat) - sizes of the queues, read/write latencies, etc.
Also it's recommended to use a dedicated OpsCenter cluster to store metrics - it could be smaller size, and doesn't require an additional license for DSE. How it said in the OpsCenter's documentation:
Important: In production environments, DataStax strongly recommends storing data in a separate DataStax Enterprise cluster.
Regarding VMs - usually it's not really recommended setup, but heavily depends on what kind of underlying hardware - number of CPUs, RAM, disk system.
Let's imagine I have a Cassandra cluster with 3 nodes, each having 100GB of available hard disk space. Replication Factor for this cluster is set to 3 and R/W CLs are set to 2, meaning I can tolerate one of my nodes going down without sacrificing consistency or availability.
Now imagine my servers have started to fill up (80GB as an example) and I would like to add another 3 servers of the same specification to my cluster, maintaining the same CLs and RFs.
My question is: after I've added the new nodes to my cluster and run the node repair tool, is it fair to assume that each of my nodes should roughly (more or less a few GBs) contain 40GB of data each?
If not, how can I add new nodes without having the fear of running out of hard disk space?
A little background of why I'm asking this question: I am developing an app that connects to a server that runs Cassandra for its data storage. As this is only developed by me, and I have limited resources in terms of money to buy servers, I've decided that I would like to buy small, cheap "servers" instead of the more expensive rack options but I'm really worried about the nodes running out of space if the disk allocation is not (at least partially)
homogenous.
Many thanks for you help,
My question is: after I've added the new nodes to my cluster and run
the node repair tool, is it fair to assume that each of my nodes
should roughly (more or less a few GBs) 40GB of data each
After also running nodetool cleanup you should see roughly 40GB of data on each node. Cleanup removes data which the node is no longer responsible for. If you don't run this command the old data will remain on the machine.
I'm running Datastax Enterprise in a cluster consisting of 3 nodes. They are all running under the same hardware: 2 Core Intel Xeon 2.2 Ghz, 7 GB RAM, 4 TB Raid-0
This should be enough for running a cluster with a light load, storing less than 1 GB of data.
Most of the time, everything is just fine but it appears that sometimes the running tasks related to the Repair Service in OpsCenter sometimes get stuck; this causes an instability in that node and an increase in load.
However, if the node is restarted, the stuck tasks don't show up and the load is at normal levels again.
Because of the fact that we don't have much data in our cluster we're using the min_repair_time parameter defined in opscenterd.conf to delay the repair service so that it doesn't complete too often.
It really seems a little bit weird that the tasks that says that are marked as "Complete" and are showing a progress of 100% don't go away, and yes, we've waited hours for them to go away but they won't; the only way that we've found to solve this is to restart the nodes.
Edit:
Here's the output from nodetool compactionstats
Edit 2:
I'm running under Datastax Enterprise v. 4.6.0 with Cassandra v. 2.0.11.83
Edit 3:
This is output from dstat on a node that behaving normally
This is output from dstat on a node with stucked compaction
Edit 4:
Output from iostat on node with stucked compaction, see the high "iowait"
azure storage
Azure divides disk resources among storage accounts under an individual user account. There can be many storage accounts in an individual user account.
For the purposes of running DSE [or cassandra], it is important to note that a single storage account should not should not be shared between more than two nodes if DSE [or cassandra] is configured like the examples in the scripts in this document. This document configures each node to have 16 disks. Each disk has a limit of 500 IOPS. This yields 8000 IOPS when configured in RAID-0. So, two nodes will hit 16,000 IOPS and three would exceed the limit.
See details here
So, this has been an issue that have been under investigation for a long time now and we've found a solution, however, we aren't sure what the underlaying problem that were causing the issues were but we got a clue even tho that, nothing can be confirmed.
Basically what we did was setting up a RAID-0 also known as Striping consisting of four disks, each at 1 TB of size. We should have seen somewhere 4x one disks IOPS when using the Stripe, but we didn't, so something was clearly wrong with the setup of the RAID.
We used multiple utilities to confirm that the CPU were waiting for the IO to respond most of the time when we said to ourselves that the node was "stucked". Clearly something with the IO and most probably our RAID-setup was causing this. We tried a few differences within MDADM-settings etc, but didn't manage to solve the problems using the RAID-setup.
We started investigating Azure Premium Storage (which still is in preview). This enables attaching disks to VMs whose underlaying physical storage actually are SSDs. So we said, well, SSDs => more IOPS, so let us give this a try. We did not setup any RAID using the SSDs. We are only using one single SSD-disk per VM.
We've been running the Cluster for almost 3 days now and we've stress tested it a lot but haven't been able to reproduce the issues.
I guess we didn't came down to the real cause but the conclusion is that some of the following must have been the underlaying cause for our problems.
Too slow disks (writes > IOPS)
RAID was setup incorrectly which caused the disks to function non-normally
These two problems go hand-in-hand and most likely is that we basically just was setting up the disks in the wrong way. However, SSDs = more power to the people, so we will definitely continue using SSDs.
If someone experience the same problems that we had on Azure with RAID-0 on large disks, don't hesitate to add to here.
Part of the problem you have is that you do not have a lot of memory on those systems and it is likely that even with only 1GB of data per node, your nodes are experiencing GC pressure. Check in the system.log for errors and warnings as this will provide clues as to what is happening on your cluster.
The rollups_60 table in the OpsCenter schema contains the lowest (minute level) granularity time series data for all your Cassandra, OS, and DSE metrics. These metrics are collected regardless of whether you have built charts for them in your dashboard so that you can pick up historical views when needed. It may be that this table is outgrowing your small hardware.
You can try tuning OpsCenter to avoid this kind of issues. Here are some options for configuration in your opscenterd.conf file:
Adding keyspaces (for example the opsc keyspace) to your ignored_keyspaces setting
You can also decrease the TTL on this table by tuning the 1min_ttlsetting
Sources:
Opscenter Config DataStax docs
Metrics Config DataStax Docs